Open AccessAn Efficient Hierarchical Layered Graph Approach for Multi-Region Segmentation
Author(s) -
Leissi M. Castañeda Leon,
Krzysztof Chris Ciesielski,
Paulo A. V. Miranda
Publication year - 2019
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sibgrapi.est.2019.8301
Subject(s) - image segmentation , digraph , prior probability , computer science , tree (set theory) , segmentation , node (physics) , hierarchical database model , graph , computational complexity theory , algorithm , theoretical computer science , pattern recognition (psychology) , mathematics , artificial intelligence , data mining , combinatorics , bayesian probability , structural engineering , engineering
We proposed a novel efficient seed-based method for the multiple region segmentation of images based on graphs, named Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). It uses a tree of the relations between the image objects, represented by a node. Each tree node may contain different individual high-level priors and defines a weighted digraph, named as layer. The layer graphs are then integrated into a hierarchical graph, considering the hierarchical relations of inclusion and exclusion. A single energy optimization is performed in the hierarchical layered weighted digraph leading to globally optimal results satisfying all the high-level priors. The experimental evaluations of HLOIFT and its extensions, on medical, natural and synthetic images, indicate promising results comparable to the state-of-the-art methods, but with lower computational complexity. Compared to hierarchical segmentation by the min-cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios, and has a better running time.